5,717 research outputs found
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
A Robust Localization System for Inspection Robots in Sewer Networks †
Sewers represent a very important infrastructure of cities whose state should be monitored
periodically. However, the length of such infrastructure prevents sensor networks from being
applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network.
It is capable of sensing gas concentrations and detecting failures in the network such as cracks and
holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely
geo-localized to allow the operators performing the required correcting measures. To this end, this
paper presents a robust localization system for global pose estimation on sewers. It makes use of prior
information of the sewer network, including its topology, the different cross sections traversed and
the position of some elements such as manholes. The system is based on a Monte Carlo Localization
system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into
account the sewer network topology for discarding wrong hypotheses. Additionally, the localization
is further refined with novel updating steps proposed in this paper which are activated whenever
a discrete element in the sewer network is detected or the relative orientation of the robot over the
sewer gallery could be estimated. Each part of the system has been validated with real data obtained
from the sewers of Barcelona. The whole system is able to obtain median localization errors in the
order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art
Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the
approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2
Graph Optimization Approach to Range-based Localization
In this paper, we propose a general graph optimization based framework for
localization, which can accommodate different types of measurements with
varying measurement time intervals. Special emphasis will be on range-based
localization. Range and trajectory smoothness constraints are constructed in a
position graph, then the robot trajectory over a sliding window is estimated by
a graph based optimization algorithm. Moreover, convergence analysis of the
algorithm is provided, and the effects of the number of iterations and window
size in the optimization on the localization accuracy are analyzed. Extensive
experiments on quadcopter under a variety of scenarios verify the effectiveness
of the proposed algorithm and demonstrate a much higher localization accuracy
than the existing range-based localization methods, especially in the altitude
direction
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
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